The Johns Hopkins University provide data on the coronavirus crisis, reportin the daily number of confirmed cases, deaths and recovered patients.

Spatial Spreading of the pandemy

Data are available from 2020-01-22 to 2020-04-02 for 181 countries.

Mapping the data

The sf library, allows to produce map easily.

Mapping the data on a world map requires two merge the world shapefile and the Johns Hoppkins data as proposed in the following code. The matching between the two databases is done by creating a country_id identification (country name in lower case). The few mismatches in the 2 databases are idenified and corrected.

## Reading layer `TM_WORLD_BORDERS-0.3' from data source `/home/metienne/git/MarieEtienne.github.io/datasets/shape_dir/TM_WORLD_BORDERS-0.3.shp' using driver `ESRI Shapefile'
## Simple feature collection with 246 features and 11 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.6236
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## [1] czechia          diamond princess iran             korea, south    
## [5] moldova          us              
## 181 Levels: afghanistan albania algeria andorra ... zimbabwe

Finally the three levels of severity are presented in the map below

Time series data

Confirmed cases

We can represent the evolution of coronavirus confirmed cases for the countries with high severity.

Too many data on the graph , focus on the countries with High level of severity.

Evolution of the number of confirmed cases

and with a log scale

Evolution of the number of confirmed cases in log2 scale

As the pandemy does not start to spread at the same date on every country, one needs to define some new origine of time for every country, in order to allow the comparison between countries.

Change time origine : Accounting for the delay in the spread of the pandemy, we could decide to represent time since the first instant where the 80\(^\mbox{th}\) cases were confirmed.

Presented below is the number of confirmed cases.

Evolution of confirmed cases, time elapsed since the 80th reported cases

Evolution of the number of deaths

Presented below is the number of deaths. Those time series are more reliable, as they do not depend on the test effort.

Evolution of the number of deaths, time elapsed since the 80th reported cases

As the number of confirmed cases might not be the best indicator of the spread of the pandemy, we choose, the time of the tenth death as the origin of date.

Evolution of the number of deaths, time elapsed since the 10th death

Evolution of the number of new deaths

As the pandemy is spreading, the number of deaths is expected to grow bigger every day before slowing down. Therefore the number of new daths, which also the derivative of the number of deaths, could be a good indicator of the stage of the pandemy.

Evolution of the number of new deaths, time elapsed since the 10th death, log2 scale